INTRODUCTION: Computed tomography (CT) and magnetic resonance imaging (MRI) can provide detailed anatomic structures and quantitative function information for chronic obstructive pulmonary disease (COPD). OBJECTIVES: To prospectively clarify characteristics of pulmonary function test (PFT), CT volume parameters and magnetic resonance (MR) perfusion imaging in COPD patients with different high-resolution computed tomography (HRCT) phenotypes. METHODS: Sixty-two patients performed PFT, CT and MR perfusion imaging. COPD was classified into three phenotypes according to HRCT quantitative findings: A, E and M phenotype. Total lung volume (TLV), total emphysema volume (TEV) and emphysema index (EI) were quantitated by HRCT. In cases of perfusion defects (PDs), the shape and size were evaluated. The contrast between the normal lung and PDs was quantified by calculating their signal intensity ratio (RSI = SIPD /SInormal ). The correlation was performed between PFT, CT and MR perfusion. RESULTS: There were 42 A phenotype, 9 E phenotype and 11 M phenotype. There was significant difference in forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) between A and M phenotype (P < 0.05). TEV and EI of A phenotype (0.4 ± 0.4 L and 8.0% ± 4.3%) were lower than those of E (1.0 ± 0.3 L and 18.6% ± 3.2%) or M phenotype (0.9 ± 0.2 L and 17.5% ± 1.7%). MR perfusion images showed circumscribed or diffuse patchy PDs. RSI of A phenotype was higher than that of E phenotype (20.3% ± 8.5% vs 11.8% ± 5.4%; P = 0.006). TEV and EI were moderate negatively correlated with diffusion function parameters. RSI was strongly correlated with FEV1% (A) and FEV1/FVC (M). FEV1/FVC was strongly correlated with TEV or EI (E). CONCLUSION: There were different features and correlations between PFT, CT volume and MR perfusion in different phenotype, indicating each phenotype may have novel imaging method guiding clinical management.
INTRODUCTION: Computed tomography (CT) and magnetic resonance imaging (MRI) can provide detailed anatomic structures and quantitative function information for chronic obstructive pulmonary disease (COPD). OBJECTIVES: To prospectively clarify characteristics of pulmonary function test (PFT), CT volume parameters and magnetic resonance (MR) perfusion imaging in COPDpatients with different high-resolution computed tomography (HRCT) phenotypes. METHODS: Sixty-two patients performed PFT, CT and MR perfusion imaging. COPD was classified into three phenotypes according to HRCT quantitative findings: A, E and M phenotype. Total lung volume (TLV), total emphysema volume (TEV) and emphysema index (EI) were quantitated by HRCT. In cases of perfusion defects (PDs), the shape and size were evaluated. The contrast between the normal lung and PDs was quantified by calculating their signal intensity ratio (RSI = SIPD /SInormal ). The correlation was performed between PFT, CT and MR perfusion. RESULTS: There were 42 A phenotype, 9 E phenotype and 11 M phenotype. There was significant difference in forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) between A and M phenotype (P < 0.05). TEV and EI of A phenotype (0.4 ± 0.4 L and 8.0% ± 4.3%) were lower than those of E (1.0 ± 0.3 L and 18.6% ± 3.2%) or M phenotype (0.9 ± 0.2 L and 17.5% ± 1.7%). MR perfusion images showed circumscribed or diffuse patchy PDs. RSI of A phenotype was higher than that of E phenotype (20.3% ± 8.5% vs 11.8% ± 5.4%; P = 0.006). TEV and EI were moderate negatively correlated with diffusion function parameters. RSI was strongly correlated with FEV1% (A) and FEV1/FVC (M). FEV1/FVC was strongly correlated with TEV or EI (E). CONCLUSION: There were different features and correlations between PFT, CT volume and MR perfusion in different phenotype, indicating each phenotype may have novel imaging method guiding clinical management.
Authors: Katja Hueper; Jens Vogel-Claussen; Megha A Parikh; John H M Austin; David A Bluemke; James Carr; Jiwoong Choi; Thomas A Goldstein; Antoinette S Gomes; Eric A Hoffman; Steven M Kawut; Joao Lima; Erin D Michos; Wendy S Post; Ming Jack Po; Martin R Prince; Kiang Liu; Dan Rabinowitz; Jan Skrok; Ben M Smith; Karol Watson; Youbing Yin; Alan M Zambeli-Ljepovic; R Graham Barr Journal: Am J Respir Crit Care Med Date: 2015-09-01 Impact factor: 21.405
Authors: Eric A Hoffman; David A Lynch; R Graham Barr; Edwin J R van Beek; Grace Parraga Journal: J Magn Reson Imaging Date: 2015-07-22 Impact factor: 4.813
Authors: Mark Bryant; Sebastian Ley; Ralf Eberhardt; Ravi Menezes; Felix Herth; Oliver Sedlaczek; Hans-Ulrich Kauczor; Julia Ley-Zaporozhan Journal: Eur Radiol Date: 2014-08-28 Impact factor: 5.315
Authors: John D Newell; Matthew K Fuld; Thomas Allmendinger; Jered P Sieren; Kung-Sik Chan; Junfeng Guo; Eric A Hoffman Journal: Invest Radiol Date: 2015-01 Impact factor: 6.016